Intra-patient and inter-patient seizure prediction from spatial-temporal EEG features

Shuoxin Ma, Daniel Bliss

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an algorithm for both intra-patient and inter-patient seizure prediction from invasive electroencephalography (EEG) is proposed and tested. Multi-channel EEG signal are pre-processed, windowed and built into spatial-temporal covariance matrices. Multivariate features are extracted from these matrices, then reduced in dimensionality by principle component analysis (PCA). A support vector machine (SVM) system is trained with the features of classified segments of data to predict the un-classified segments. The cross-validation test shows that the proposed algorithm achieves significantly better performance than that achieved in existing literatures, with the area under receiver operating characteristic (ROC) curve of 0.977 for intra-patient and 0.822 for inter-patient prediction. The significance test further proves that the result is statistically reliable for intra-patient prediction with p-value of 0.00, and well considerable for inter-patient prediction with p-value of 0.08.

Original languageEnglish (US)
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages194-199
Number of pages6
ISBN (Electronic)9781479982974
DOIs
StatePublished - Apr 24 2015
Event48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 2 2014Nov 5 2014

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Other

Other48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/2/1411/5/14

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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